qq.plot(x, ...)
qqp(...)
qq.plot.default(x, distribution="norm",
ylab=deparse(substitute(x)),
xlab=paste(distribution, "quantiles"), main="", las=1,
envelope=0.95, labels=F, col=palette()[2], lwd=2, pch=1,
line=c("quartiles", "robust"), ...)
qq.plot.lm(x, main="", xlab="t Quantiles",
line=c("quartiles", "robust"),
ylab=paste("Studentized Residuals(", deparse(substitute(x)), ")",
sep = ""), las=1,
simulate=F, envelope=0.95, labels=F, reps=100,
col=palette()[2], lwd=2, pch=1, ...)lm object.norm for the
normal distribution; t for the t-distribution.FALSE for no envelope.FALSE for no labels.0, ticks labels are drawn parallel to the
axis; set to 1 (the default) for horizontal labels (see par).1
(a circle, see par).2 (see par).
Confidence envelopes are drawn at half this line width."quartiles" to pass a line through the quartile-pairs, or
"robust" for a robust-regression line; the latter uses the rlm
function in the MASS package.TRUE calculate confidence envelope by parametric bookstrap;
for lm object only. The method is due to Atkinson (1985).df to be passed to the appropriate quantile function.NULL. These functions are used only for their side effect (to make a graph).q and d, respectively) may be used.
Studentized residuals are plotted against the
appropriate t-distribution.
The function qqp is an abbreviation for qq.plot.qqplot, qqnorm,
qqlinex<-rchisq(100, df=2)
qq.plot(x)
qq.plot(x, dist="chisq", df=2)
data(Ornstein)
mod<-lm(interlocks~assets+sector+nation, data=Ornstein)
qq.plot(mod, sim=T)Run the code above in your browser using DataLab